{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e9ac128",
   "metadata": {},
   "source": [
    "# Thermal conductivity from BTE\n",
    "\n",
    "Here, we provide an introductory tutorial on how to use [phono3py](https://phonopy.github.io/phono3py/) to compute the thermal conductivity using a neuroevolution potential (NEP) via the Boltzmann transport equation (BTE).\n",
    "The system under study is graphite represented by a NEP model developed in [this paper](http://doi.org/10.1021/acsnano.3c09717).\n",
    "Note that `phono3py` also allows you to compute other phonon properties such as, e.g., phonon lifetimes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7bd414e",
   "metadata": {},
   "source": [
    "All models and structures required for running this and the other tutorial notebooks can be obtained from [Zenodo](https://zenodo.org/record/10658778).\n",
    "The files are also available in the `tutorials/` folder in the [GitLab repository](https://gitlab.com/materials-modeling/calorine/-/tree/master/tutorials)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e52d2b",
   "metadata": {},
   "source": [
    "## Workflow\n",
    "\n",
    "The steps required are:\n",
    "\n",
    "1. Relax the primitive cell\n",
    "2. Compute the force constants (`fc2.hdf5`, `fc3.hdf5`) using the NEP model for all structures generated by `phono3py`\n",
    "3. Run the thermal conductivity calculation\n",
    "\n",
    "Since `phono3py` does not have a python API we will call it through its command line interface via the `os.system` function.\n",
    "\n",
    "\n",
    "## BTE computational parameters\n",
    "\n",
    "**Note 1:**\n",
    "In order to achive converged force constants one must use a sufficiently large supercell, the size of which is specified via the `--dim` option.\n",
    "Here, we, however, use a rather small supercell in order for the tutorial to run faster.\n",
    "\n",
    "**Note 2:**\n",
    "Here, we use the `phono3py` default displacement amplitude of 0.03 Å when generating supercells for the force-constant extraction.\n",
    "This may not be suitable for every materials.\n",
    "\n",
    "**Note 3:**\n",
    "In order to achieve convergence of the thermal conductivity one should use a larger q-point mesh (`--mesh`), but here we use a rather small mesh in order for the tutorial to complete within an acceptable time.\n",
    "\n",
    "**Note 4:**\n",
    "In order to obtain accurate predictions for the thermal conductivity in graphite one should employ the `--lbte` (see [here](https://phonopy.github.io/phono3py/direct-solution.html) for more information).\n",
    "This method is, however, more costly both computationally and with respect to memory.\n",
    "For simpliticity we therefore limit ourselves to the relaxation time approximation (RTA), which is invoked via the `--bterta` option.\n",
    "\n",
    "## Miscellaneous\n",
    "\n",
    "Additional related tutorials can be found on the [hiphive website](https://hiphive.materialsmodeling.org/tutorial/compute_third_order_properties.html) and [here](https://hiphive.materialsmodeling.org/tutorial/compute_third_order_properties.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "757e4176",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "from glob import glob\n",
    "\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "from ase.io import read, write\n",
    "from calorine.calculators import CPUNEP\n",
    "from calorine.tools import relax_structure\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1bb7a66-8804-413c-a16e-73d601f4d41f",
   "metadata": {},
   "source": [
    "## Preparations\n",
    "\n",
    "We start out by setting up the working directory for the `phono3py` calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b12922b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root directory: /home/erhart/repos/calorine/tutorials\n",
      "Working directory: /home/erhart/repos/calorine/tutorials/phono3py_workdir\n"
     ]
    }
   ],
   "source": [
    "root_dir = os.getcwd()  # here we set the root directory\n",
    "work_dir = os.path.join(root_dir, 'phono3py_workdir')\n",
    "shutil.rmtree(work_dir, ignore_errors=True)  # Deletes current working dir\n",
    "os.mkdir(work_dir)\n",
    "os.chdir(work_dir)\n",
    "\n",
    "print(f'Root directory: {root_dir}')\n",
    "print(f'Working directory: {work_dir}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bccaf0d",
   "metadata": {},
   "source": [
    "## Relaxation of primitive cell\n",
    "\n",
    "First, we relax the primitive graphite unit cell (4-atoms) with respect to ionic positions and cell metric.\n",
    "To obtain accurate force constants it is recommended to use a tight convergence condition, i.e., a small value for the termination condition `fmax`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec64b525-00d9-4f85-b012-64a7fc6c430b",
   "metadata": {},
   "outputs": [],
   "source": [
    "potential_filename = os.path.join(root_dir, 'nep-graphite-CX.txt')\n",
    "prim = read(os.path.join(root_dir, 'graphite-prim.xyz'))\n",
    "prim.calc = CPUNEP(potential_filename)\n",
    "relax_structure(prim, fmax=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f9bdfa",
   "metadata": {},
   "source": [
    "## Construction of force constants\n",
    "\n",
    "Next, we construct the force constants.\n",
    "Here, we use the approach implemented in `phono3py`, which generates the second and third force constants by systematic enumeration and numerical evaluation of the derivatives involved.\n",
    "Especially for more complex materials, say systems with larger unit cells and/or lower symmetry, it can be beneficial to use regression based approaches such as the one implemented in, e.g., [hiphive](https://hiphive.materialsmodeling.org/).\n",
    "\n",
    "First, we call `phono3py` to generate a series of configurations (supercells) with systematic displacements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5dabc5e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running command: phono3py -d --dim=\"4 4 2\" --dim-fc2=\"4 4 2\"\n",
      "        _                      _____\n",
      "  _ __ | |__   ___  _ __   ___|___ / _ __  _   _\n",
      " | '_ \\| '_ \\ / _ \\| '_ \\ / _ \\ |_ \\| '_ \\| | | |\n",
      " | |_) | | | | (_) | | | | (_) |__) | |_) | |_| |\n",
      " | .__/|_| |_|\\___/|_| |_|\\___/____/| .__/ \\__, |\n",
      " |_|                                |_|    |___/ \n",
      "                                       2.6.0\n",
      "\n",
      "-------------------------[time 2023-04-18 18:57:09]-------------------------\n",
      "Compiled with OpenMP support (max 8 threads).\n",
      "Python version 3.9.12\n",
      "Spglib version 1.16.5\n",
      "\n",
      "Unit cell was read from \"POSCAR\".\n",
      "Displacement distance: 0.03\n",
      "Number of displacements: 1220\n",
      "Number of displacements for special fc2: 1220\n",
      "Spacegroup: P6_3/mmc (194)\n",
      "Displacement dataset was written in \"phono3py_disp.yaml\".\n",
      "-------------------------[time 2023-04-18 18:57:11]-------------------------\n",
      "                 _\n",
      "   ___ _ __   __| |\n",
      "  / _ \\ '_ \\ / _` |\n",
      " |  __/ | | | (_| |\n",
      "  \\___|_| |_|\\__,_|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# supercell size\n",
    "dim = (4, 4, 2)\n",
    "\n",
    "cmd = f'phono3py -d --dim=\"{dim[0]} {dim[1]} {dim[2]}\" --dim-fc2=\"{dim[0]} {dim[1]} {dim[2]}\"'\n",
    "\n",
    "write('POSCAR', prim)\n",
    "\n",
    "print(f'Running command: {cmd}')\n",
    "os.system(cmd);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3ca4a7-ead1-469e-807a-e441f0fd42b7",
   "metadata": {},
   "source": [
    "Next we compute the forces for the second-order force constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d94a407-96dd-4847-bee2-a00e27641916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FC2: Calculating supercell     0 / 4, f_max  1.85218\n",
      "FC2: Calculating supercell     1 / 4, f_max  0.42094\n",
      "FC2: Calculating supercell     2 / 4, f_max  1.85890\n",
      "FC2: Calculating supercell     3 / 4, f_max  0.41891\n"
     ]
    }
   ],
   "source": [
    "fnames = sorted(glob('POSCAR_FC2-*'))\n",
    "forces_data = []\n",
    "for it, fname in enumerate(fnames):\n",
    "    structure = read(fname)\n",
    "    structure.calc = CPUNEP(potential_filename)\n",
    "    forces = structure.get_forces()\n",
    "    forces_data.append(forces)\n",
    "    print(f'FC2: Calculating supercell {it:5d} / {len(fnames)}, f_max {np.max(np.abs(forces)):8.5f}')\n",
    "forces_data = np.array(forces_data).reshape(-1, 3)\n",
    "np.savetxt('FORCES_FC2', forces_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c74287-e6ac-4e63-8569-8a35f9cfa192",
   "metadata": {},
   "source": [
    "Followed by the forces for the third-order force constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f57e7d7-e1cf-4a28-963c-4fb5a56020b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FC3: Calculating supercell     0 / 1220, f_max=  1.85218\n",
      "FC3: Calculating supercell   100 / 1220, f_max=  1.85218\n",
      "FC3: Calculating supercell   200 / 1220, f_max=  1.85217\n",
      "FC3: Calculating supercell   300 / 1220, f_max=  1.85213\n",
      "FC3: Calculating supercell   400 / 1220, f_max=  1.85216\n",
      "FC3: Calculating supercell   500 / 1220, f_max=  0.43240\n",
      "FC3: Calculating supercell   600 / 1220, f_max=  1.57674\n",
      "FC3: Calculating supercell   700 / 1220, f_max=  1.85890\n",
      "FC3: Calculating supercell   800 / 1220, f_max=  1.85313\n",
      "FC3: Calculating supercell   900 / 1220, f_max=  1.85893\n",
      "FC3: Calculating supercell  1000 / 1220, f_max=  1.85861\n",
      "FC3: Calculating supercell  1100 / 1220, f_max=  1.11016\n",
      "FC3: Calculating supercell  1200 / 1220, f_max=  1.85924\n"
     ]
    }
   ],
   "source": [
    "fnames = sorted(glob('POSCAR-*'))\n",
    "forces_data = []\n",
    "for it, fname in enumerate(fnames):\n",
    "    structure = read(fname)\n",
    "    structure.calc = CPUNEP(potential_filename)\n",
    "    forces = structure.get_forces()\n",
    "    forces_data.append(forces)\n",
    "    if it % 100 == 0:\n",
    "        print(f'FC3: Calculating supercell {it:5d} / {len(fnames)}, f_max= {np.max(np.abs(forces)):8.5f}')\n",
    "forces_data = np.array(forces_data).reshape(-1, 3)\n",
    "np.savetxt('FORCES_FC3', forces_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68984b1-0d9a-4562-9252-6017572e05ec",
   "metadata": {},
   "source": [
    "Finally, we can combine the forces to construct force constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "372a3c44-1ddf-4954-92e9-a5b7f33fb1cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running command: phono3py --cfc --hdf5-compression gzip --dim=\"4 4 2\" --dim-fc2=\"4 4 2\"\n",
      "        _                      _____\n",
      "  _ __ | |__   ___  _ __   ___|___ / _ __  _   _\n",
      " | '_ \\| '_ \\ / _ \\| '_ \\ / _ \\ |_ \\| '_ \\| | | |\n",
      " | |_) | | | | (_) | | | | (_) |__) | |_) | |_| |\n",
      " | .__/|_| |_|\\___/|_| |_|\\___/____/| .__/ \\__, |\n",
      " |_|                                |_|    |___/ \n",
      "                                       2.6.0\n",
      "\n",
      "-------------------------[time 2023-04-18 18:57:36]-------------------------\n",
      "Compiled with OpenMP support (max 8 threads).\n",
      "Python version 3.9.12\n",
      "Spglib version 1.16.5\n",
      "----------------------------- General settings -----------------------------\n",
      "Run mode: None\n",
      "HDF5 data compression filter: gzip\n",
      "Crystal structure was read from \"POSCAR\".\n",
      "Supercell (dim): [4 4 2]\n",
      "Phonon supercell (dim-fc2): [4 4 2]\n",
      "Spacegroup: P6_3/mmc (194)\n",
      "Use -v option to watch primitive cell, unit cell, and supercell structures.\n",
      "----------------------------- Force constants ------------------------------\n",
      "Imposing translational and index exchange symmetry to fc2: False\n",
      "Imposing translational and index exchange symmetry to fc3: False\n",
      "Displacement dataset for fc3 was read from \"POSCAR\".\n",
      "Sets of supercell forces were read from \"FORCES_FC3\".\n",
      "Computing fc3[ 1, x, x ] using numpy.linalg.pinv with displacements:\n",
      "    [ 0.0300  0.0000  0.0000]\n",
      "    [ 0.0000  0.0000  0.0300]\n",
      "Computing fc3[ 33, x, x ] using numpy.linalg.pinv with displacements:\n",
      "    [ 0.0300  0.0000  0.0000]\n",
      "    [ 0.0000  0.0000  0.0300]\n",
      "Expanding fc3.\n",
      "Writing fc3 to \"fc3.hdf5\".\n",
      "Max drift of fc3: 87.142189 (yxx) 0.627641 (yxx) -0.000000 (yxx)\n",
      "Sets of supercell forces were read from \"FORCES_FC2\".\n",
      "Writing fc2 to \"fc2.hdf5\".\n",
      "Max drift of fc2: 0.034216 (xx) 0.000000 (yy)\n",
      "----------- None of ph-ph interaction calculation was performed. -----------\n",
      "Summary of calculation was written in \"phono3py.yaml\".\n",
      "-------------------------[time 2023-04-18 18:57:38]-------------------------\n",
      "                 _\n",
      "   ___ _ __   __| |\n",
      "  / _ \\ '_ \\ / _` |\n",
      " |  __/ | | | (_| |\n",
      "  \\___|_| |_|\\__,_|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cmd = f'phono3py --cfc --hdf5-compression gzip --dim=\"{dim[0]} {dim[1]} {dim[2]}\" --dim-fc2=\"{dim[0]} {dim[1]} {dim[2]}\"'\n",
    "print(f'Running command: {cmd}')\n",
    "os.system(cmd);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8d1da7",
   "metadata": {},
   "source": [
    "## BTE calculation\n",
    "\n",
    "Having prepared the force constants we are now in a position to carry out the calculation of the lattice thermal conductivity.\n",
    "Note that the following cell can take a couple of minutes to run.\n",
    "Since by default `phono3py` produces a lot of output during the execution of this task, the output is turned off here via the `-q` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a531b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running command: phono3py -q --fc2 --fc3 --bterta --dim=\"4 4 2\" --dim-fc2=\"4 4 2\" --tmin=10 --tmax=1000 --tstep=10 --mesh \"16 16 8\"\n"
     ]
    }
   ],
   "source": [
    "mesh = [16, 16, 8]  # q-point mesh\n",
    "T_min, T_max = 10, 1000  # temperature range\n",
    "T_step = 10  # spacing of temperature points\n",
    "\n",
    "cmd = f'phono3py -q --fc2 --fc3 --bterta --dim=\"{dim[0]} {dim[1]} {dim[2]}\" --dim-fc2=\"{dim[0]} {dim[1]} {dim[2]}\"'\\\n",
    "      f' --tmin={T_min} --tmax={T_max} --tstep={T_step} --mesh \"{mesh[0]} {mesh[1]} {mesh[2]}\"'\n",
    "print(f'Running command: {cmd}')\n",
    "\n",
    "os.system(cmd);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08100f8e-9b23-461a-bb4d-95c2ae802228",
   "metadata": {},
   "source": [
    "After the calculation has concluded, we return to the original directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27b7a288",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb7ffd0d",
   "metadata": {},
   "source": [
    "## Analysis of  results\n",
    "\n",
    "We can now read the results of the BTE calculations from the `kappa-<mesh>.hdf5` file (where `<mesh>` specicifies the q-point mesh used for the calculation).\n",
    "For more information about the content of the `kappa-<mesh>.hdf5` file see [here](https://phonopy.github.io/phono3py/hdf5_howto.html#)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4cb0328c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data:\n",
      "  frequency                 : array-shape (150, 12)\n",
      "  gamma                     : array-shape (100, 150, 12)\n",
      "  grid_point                : array-shape (150,)\n",
      "  group_velocity            : array-shape (150, 12, 3)\n",
      "  gv_by_gv                  : array-shape (150, 12, 6)\n",
      "  heat_capacity             : array-shape (100, 150, 12)\n",
      "  kappa                     : array-shape (100, 6)\n",
      "  kappa_unit_conversion     : array-shape ()\n",
      "  mesh                      : array-shape (3,)\n",
      "  mode_kappa                : array-shape (100, 150, 12, 6)\n",
      "  qpoint                    : array-shape (150, 3)\n",
      "  temperature               : array-shape (100,)\n",
      "  version                   : array-shape ()\n",
      "  weight                    : array-shape (150,)\n"
     ]
    }
   ],
   "source": [
    "fobj = h5py.File(f'{work_dir}/kappa-m{mesh[0]}{mesh[1]}{mesh[2]}.hdf5')\n",
    "print('Data:')\n",
    "for key in fobj:\n",
    "    print(f'  {key:25} : array-shape {fobj[key].shape}')\n",
    "\n",
    "temperatures = fobj['temperature'][:]\n",
    "kappa = fobj['kappa'][:]\n",
    "gamma = fobj['gamma'][:]\n",
    "freqs = fobj['frequency'][:]\n",
    "fobj.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94652dcd-24f7-42a3-bb77-fcff240658ce",
   "metadata": {},
   "source": [
    "Here, `kappa` is an array of shape `(number of temperatures, 6)`, where the 6 indices corresponds to the `xx`, `yy`, `zz`, `yz`, `xz`, and `xy` elements of the thermal conductivity tensor.\n",
    "\n",
    "For the temperature dependence of the thermal conductivity, we obtain the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7f19f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 532x420 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3.8, 3), dpi=140)\n",
    "\n",
    "ax.plot(temperatures, kappa[:, 0], '-', label=r'$\\kappa_{xx}$')\n",
    "ax.plot(temperatures, kappa[:, 1], '--', label=r'$\\kappa_{yy}$')\n",
    "ax.plot(temperatures, kappa[:, 2], '-', label=r'$\\kappa_{zz}$')\n",
    "ax.legend(loc='upper right', frameon=False)\n",
    "\n",
    "ax.set_xlim([50, temperatures.max()])\n",
    "ax.set_ylim([0, np.max(kappa)*1.05])\n",
    "\n",
    "ax.set_xlabel('Temperature (K)')\n",
    "ax.set_ylabel('Thermal conductivty (W/m/K)')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a6e43a-b07e-41a6-abd1-b8d09ab72bb0",
   "metadata": {},
   "source": [
    "Similarly, we can visualize lifetimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e763a3ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature 100.0\n",
      "Temperature 300.0\n",
      "Temperature 1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 532x420 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3.8, 3), dpi=140)\n",
    "\n",
    "T_index = 29\n",
    "T_indices = [9, 29, 99]\n",
    "for T_index in T_indices:\n",
    "\n",
    "    print(f'Temperature {temperatures[T_index]}')\n",
    "    g = gamma[T_index].flatten()\n",
    "    g = np.where(g > 0.0, g, -1)\n",
    "    lifetimes = np.where(g > 0.0, 1.0 / (2 * 2 * np.pi * g), np.nan)\n",
    "\n",
    "    ax.semilogy(freqs.flatten(), lifetimes, 'o', label=f'T={temperatures[T_index]:.0f} K', alpha=0.5, markersize=2)\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False)\n",
    "ax.set_xlabel('Phonon frequency (THz)')\n",
    "ax.set_ylabel('Phonon lifetime (ps)')\n",
    "\n",
    "ax.set_xlim([0, freqs.max()*1.02])\n",
    "\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
